An Information Appraisal Procedure: Endows Reliable Online Parameter Identification to Lithium-Ion Battery Model

Autor:	Jinhao Meng, Xinrong Huang, Shunliang Wang, Yingmin Zhang, Ping Liu, Xinghao Du, Tianqi Liu
Rok vydání:	2022
Předmět:	Recursive least squares filter Battery (electricity) Identification (information) Boosting (machine learning) Control and Systems Engineering Computer science Noise (signal processing) Equivalent circuit Electrical and Electronic Engineering Algorithm Upper and lower bounds Cramér–Rao bound
Zdroj:	IEEE Transactions on Industrial Electronics. 69:5889-5899
ISSN:	1557-9948 0278-0046
Popis:	Online parameter identification is vital for boosting the accuracy of the battery Equivalent Circuit Model (ECM) under dynamic profiles. However, traditional Recursive Least Squares (RLS) method easily decays with the noise corruption from sensors or insufficient exciting signal in reality, which further limits the performance of ECM in battery modeling and states estimation. This paper thus proposes a reliable online parameter identification method for battery ECM, which utilizes a well-designed Information Appraisal Procedure (IAP) based on the Fisher-information-based Cramer-Rao Lower Bound (CRLB). Without increasing much computing complexity, a comprehensive appraisal indicator, derived recursively from CRLB, enables a new mechanism for online parameter updating. Simulation and experimental results prove the validity of the proposed method under different driving cycles, temperatures, and aging conditions. The results show that the identification accuracy of the proposed method has been significantly improved comparing with a typical RLS and a Multiple Adaptive Forgetting Factors RLS (MAFF-RLS) method.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::9e10f3f632b51861d46e07be73cd4d31 https://doi.org/10.1109/tie.2021.3091920 Zobrazit plný text záznamu